发表机构
Universitat Politècnica de València; CentraleSupélec; University of Bologna（瓦伦西亚理工大学; 巴黎中央理工-高等电力学院; 博洛尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对5G NR网络中SINR准确预测难题，提出仅用SSB和CSI-RS的RSRP的监督学习方法，经3GPP合规数据集验证，活跃CSI-RS波束过滤可提升精度并降维，为主动网络优化提供可行方案。
AI 中文摘要
预测用户设备（UE）性能对于主动网络控制、资源管理和数字孪生沙箱至关重要。然而，基于波束的5G新无线电（NR）网络固有的灵活性和复杂性使准确的性能预测极具挑战性。本文提出一种数据驱动方法，仅依赖标准化参考信号测量值，即同步信号块（SSB）和信道状态信息参考信号（CSI-RS）的参考信号接收功率（RSRP），来预测平均下行链路信干噪比（SINR）。我们将该预测表述为监督学习问题，并使用符合第三代合作伙伴计划（3GPP）标准的合成数据集评估各种输入特征表示。分析表明，基于活跃CSI-RS波束过滤测量值可显著提升预测精度，同时降低输入维度，这种感知活跃性的策略证明了机器学习模型在主动网络优化中的强大可行性。
英文摘要
Predicting user equipment (UE) performance is essential for proactive network control, resource management, and digital twin sandboxes. However, the inherent flexibility and complexity of beam-based 5G new radio (NR) networks make accurate performance forecasting highly challenging. This paper proposes a data-driven approach to predict the average downlink signal-to-interference-plus-noise ratio (SINR) relying exclusively on standardized reference-signal measurements, namely synchronization signal block (SSB) and channel state information-reference signal (CSI-RS) reference signal received power (RSRP). We formulate this prediction as a supervised learning problem and evaluate various input feature representations using a third generation partnership project (3GPP)-compliant synthetic dataset. Our analysis reveals that filtering measurements based on active CSI-RS beams significantly enhances prediction accuracy while reducing input dimensionality. This activity-aware strategy demonstrates the strong viability of machine learning models for proactive network optimization.
